abstract = "A key challenge in program synthesis concerns how to
efficiently search for the desired program in the space
of possible programs. We propose a general approach to
accelerate search-based program synthesis by biasing
the search towards likely programs. Our approach
targets a standard formulation, syntax-guided synthesis
(SyGuS), by extending the grammar of possible programs
with a probabilistic model dictating the likelihood of
each program. We develop a weighted search algorithm to
efficiently enumerate programs in order of their
likelihood. We also propose a method based on transfer
learning that enables to effectively learn a powerful
model, called probabilistic higher-order grammar, from
known solutions in a domain. We have implemented our
approach in a tool called Euphony and evaluate it on
SyGuS benchmark problems from a variety of domains. We
show that Euphony can learn good models using easily
obtainable solutions, and achieves significant
performance gains over existing general-purpose as well
as domain-specific synthesizers.",
notes = "https://www.cis.upenn.edu/~alur/PLDI18.pdf Appendix:
'To find pbest, we adopt a genetic-programming like
procedure'